Exact Mixed-Integer Programming Approach for Chance-Constrained Multi-Area Reserve Sizing
(2024) IEEE Transactions on Power Systems — Vol. 39, n° 2, p. 3310-3323 (2024)
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- Authors
Papavasiliou, Anthony 
- Abstract
- An exact algorithm is developed for the chance- constrained multi-area reserve sizing problem in the presence of transmission network constraints. The problem can be cast as a two-stage stochastic mixed integer linear program using sample approximation. Due to the complicated structure of the problem, existing methods attempt to find a feasible solution based on heuristics. Existing mixed-integer algorithms that can be applied directly to a two-stage stochastic program can only address small- scale problems that are not practical. We have found a minimal description of the projection of our problem onto the space of the first-stage variables. This enables us to directly apply more general Integer Programming techniques for mixing sets, that arise in chance-constrained problems. Combining the advantages of the minimal projection and the strengthening reformulation from IP techniques, our method can tackle real-world problems. We specifically consider a case study of the 10-zone Nordic network with 100,000 scenarios where the optimal solution can be found in approximately 5 minutes.
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Citations
Cho, J., & Papavasiliou, A. (2024). Exact Mixed-Integer Programming Approach for Chance-Constrained Multi-Area Reserve Sizing. IEEE Transactions on Power Systems, 39(2), 3310-3323. https://doi.org/10.1109/TPWRS.2023.3279692 (Original work published 2024)